实战

根据original_data 样本,建立模型,对test_data的图片进行普通/其他苹果判断

1. 数据增强,扩充确认为普通苹果的样本数量

• 特征提取,使用VGG16模型提取图像特征
• 图像批量处理
• Kmeans模型尝试普通/其他苹果聚类
• 基于标签数据矫正结果,并可视化
• Meanshift模型提升模型表现
• 数据降维PCA处理,提升模型表现

特点: 样本少,部分标注(且都是正样本)、负样本类别不可穷尽、图像处理

样本

原数据
普通苹果 都是一样的

#数据增强 
from keras.preprocessing.image import ImageDataGenerator
path = """E:\PycharmProjects\Learning\dataset\\original_data"""
dst_path = """E:\PycharmProjects\Learning\dataset\\gen_data"""

datagen = ImageDataGenerator(rotation_range=10,width_shift_range=0.1,
                             height_shift_range=0.02,horizontal_flip=True,
                             vertical_flip=True)
gen = datagen.flow_from_directory(path,target_size=(224,224),
                                  batch_size=2,save_to_dir=dst_path,
                                  save_prefix="gen",save_format="jpg")
#224,224 VGG 输入的大小 
for i in range(100):
    gen.next()

数据增强是对普通苹果的图片进行了旋转/平移等操作,生成的新数据

将新数据和原始数据都放到 train_data 文件夹

单张图片展示 特征提取

from keras.preprocessing.image import load_img,img_to_array
img_path = "E:\PycharmProjects\Learning\dataset\\train_data\\01.jpeg"
img = load_img(img_path,target_size=(224,224))
print(type(img))

%matplotlib inline
from matplotlib import pyplot as plt
fig1 = plt.figure(figsize=(5,5))
plt.imshow(img)

img = img_to_array(img)
type(img)

#加载模型
from keras.applications.vgg16 import VGG16
from keras.applications.vgg16 import preprocess_input
import numpy as np
model_vgg = VGG16(weights="imagenet",include_top=False)
X = np.expand_dims(img,axis=0)#增加一个维度
X = preprocess_input(X)
print(X.shape)

#特征提取
features = model_vgg.predict(X)
print(features.shape)
features = features.reshape(1,7*7*512)
print(features.shape)

扩充为批量提取

先读取train_data下的所有文件

import os 
folder = """E:\PycharmProjects\Learning\dataset\\train_data"""
dirs = os.listdir(folder)
print(dirs)
img_path = []
for i in dirs:
    #if os.path.splitext(i)[1]==".jpg":
    img_path.append(i)
img_path = [folder +"\\"+i for i in img_path]
print(img_path)

#特征提取方法
def modelProcess(img_path,model):
    img = load_img(img_path,target_size=(224,224))
    img = img_to_array(img)
    X = np.expand_dims(img,axis=0)#增加一个维度
    X = preprocess_input(X)
    X_VGG = model.predict(X)
    X_VGG = X_VGG.reshape(1,7*7*512)
    return X_VGG

features_train = np.zeros([len(img_path),7*7*512])
for i in range(len(img_path)):
    features_i = modelProcess(img_path[i],model_vgg)
    print("preprocessed:",img_path[i])
    features_train[i] = features_i
print("Done")

print(features_train.shape)
X = features_train

(228, 25088)
30个苹果中有10个是普通苹果 200个是数据增强的

Kmeans聚类

from sklearn.cluster import KMeans
cnn_kmeans = KMeans(n_clusters=2,max_iter=2000)
cnn_kmeans.fit(X)

#预测
y_predict_kmeans = cnn_kmeans.predict(X)
print(y_predict_kmeans)
#统计
from collections import Counter
print(Counter(y_predict_kmeans))

Counter({0: 121, 1: 107})
分出了两类 数量分别是121 和107 说明分的不是很好

#假设普通苹果id为1
normal_apple_id = 1

#看看训练集效果
fig2 = plt.figure(figsize=(10,40))
for i in range(45):
    for j in range(5):
        img = load_img(img_path[i*5+j])
        plt.subplot(45,5,i*5+j+1)
        plt.title("apple" if y_predict_kmeans[i*5+j] == normal_apple_id else "other")
        plt.imshow(img)
        plt.axis("off")

不大行

预测测试集

#载入数据
import os 
folder_test = """E:\PycharmProjects\Learning\dataset\\test_data"""
dirs_test = os.listdir(folder_test)
img_path_test = []
for i in dirs_test:
    #if os.path.splitext(i)[1]==".jpg":
    img_path_test.append(i)
img_path_test = [folder_test +"\\"+i for i in img_path_test]
print(img_path_test)

#数据处理
features_test = np.zeros([len(img_path_test),7*7*512])
for i in range(len(img_path_test)):
    features_i = modelProcess(img_path_test[i],model_vgg)
    print("preprocessed:",img_path_test[i])
    features_test[i] = features_i
print("Done")

X_test = features_test
print(X_test.shape)

#预测
y_predict_kmeans_test = cnn_kmeans.predict(X_test)
print(y_predict_kmeans_test)
#画图 测试集的 
fig3 = plt.figure(figsize=(10,10))
for i in range(3):
    for j in range(4):
        img = load_img(img_path[i*4+j])
        plt.subplot(3,4,i*4+j+1)
        plt.title("apple" if y_predict_kmeans[i*4+j] == normal_apple_id else "other")
        plt.imshow(img)
        plt.axis("off")

效果不太好

meanshift 聚类

from sklearn.cluster import MeanShift,estimate_bandwidth

bw = estimate_bandwidth(X,n_samples = 140)
print(bw)

cnn_ms = MeanShift(bandwidth = bw)
cnn_ms.fit(X)

#预测
y_predict_ms = cnn_ms.predict(X)
print(y_predict_ms)

#统计
from collections import Counter
print(Counter(y_predict_ms))

标签0的数据比较多 标记为普通苹果

normal_apple_id = 0

fig4 = plt.figure(figsize=(10,40))
for i in range(45):
    for j in range(5):
        img = load_img(img_path[i*5+j])
        plt.subplot(45,5,i*5+j+1)
        plt.title("apple" if y_predict_ms[i*5+j] == normal_apple_id else "other")
        plt.imshow(img)
        plt.axis("off")

效果好一些

#预测 测试集
y_predict_ms_test = cnn_ms.predict(X_test)
print(y_predict_kmeans_test)
#画图 测试集的 
fig3 = plt.figure(figsize=(10,10))
for i in range(4):
    for j in range(5):
        img = load_img(img_path[i*5+j])
        plt.subplot(4,5,i*5+j+1)
        plt.title("apple" if y_predict_ms_test[i*5+j] == normal_apple_id else "other")
        plt.imshow(img)
        plt.axis("off")

PCA 主成分分析

from sklearn.preprocessing import StandardScaler
stds = StandardScaler()
X_norm = stds.fit_transform(X)
#PCA analusis
from sklearn.decomposition import PCA
pca = PCA(n_components=200)
X_pca = pca.fit_transform(X_norm)
#降维之后的主成分
#calculate the variance ratio of each components
var_ratio = pca.explained_variance_ratio_
print(np.sum(var_ratio))
print(X_pca.shape,X.shape)
#降维了

#再meanshift
from sklearn.cluster import MeanShift,estimate_bandwidth

bw = estimate_bandwidth(X_pca,n_samples = 140) #用处理后的数据
print(bw)

cnn_pca_ms = MeanShift(bandwidth = bw)
cnn_pca_ms.fit(X_pca)

#预测
y_predict_pca_ms = cnn_pca_ms.predict(X_pca)
print(y_predict_pca_ms)

#统计
from collections import Counter
print(Counter(y_predict_pca_ms))

normal_apple_id = 0

fig4 = plt.figure(figsize=(10,40))
for i in range(45):
    for j in range(5):
        img = load_img(img_path[i*5+j])
        plt.subplot(45,5,i*5+j+1)
        plt.title("apple" if y_predict_pca_ms[i*5+j] == normal_apple_id else "other")
        plt.imshow(img)
        plt.axis("off")

效果比Kmeans的好

再预测测试集

#数据转换
X_norm_test = stds.transform(X_test)
X_pca_test = pca.transform(X_norm_test)
#预测 测试集
y_predict_pca_ms_test = cnn_pca_ms.predict(X_pca_test)
print(y_predict_pca_ms_test)

#画图 测试集的 
fig3 = plt.figure(figsize=(10,10))
for i in range(4):
    for j in range(5):
        img = load_img(img_path[i*5+j])
        plt.subplot(4,5,i*5+j+1)
        plt.title("apple" if y_predict_pca_ms_test[i*5+j] == normal_apple_id else "other")
        plt.imshow(img)
        plt.axis("off")

总结

通过数据处理,将主要信息留下,效果更好

1. 通过搭建混合模型,实现监督+无监督、机器+深度学习技术的有机结合，并在少样本的情况下建立起了有效区分的模型

• 针对少样本任务，掌握生成新数据的数据增强。
• VGG16模型拆分，提取图像特征
• 图像批量处理
• 无监督聚类算法：KMeans、Meanshift，并通过标签数据分布实现数据类别矫正
• PCA降维，剔除数据中的噪音信息、降低模型复杂度、减少模型训练时间提高模型表现